Reinforcement learning (RL) has revolutionized how we train AI, enabling breakthroughs in games, robotics, and even language models. But traditional RL faces limitations: it often requires extensive training data, lacks interpretability, and struggles with complex, real-world scenarios. Researchers are exploring a groundbreaking new approach: Natural Language Reinforcement Learning (NLRL). Imagine teaching an AI agent not through complex code, but through the power of language. NLRL reimagines core RL concepts – like objectives, policies, and value functions – as language-based constructs. This allows us to tap into the vast knowledge embedded within large language models (LLMs). Instead of relying on purely numerical rewards, NLRL agents can learn from rich, textual feedback, mimicking how humans learn through instruction and explanation. Think of it like coaching a chess player: instead of simply rewarding wins, you explain strategies, analyze moves, and provide nuanced feedback. This approach offers several advantages. It can drastically reduce the need for extensive training data by leveraging the LLM's pre-existing knowledge. It makes the AI's decision-making process more transparent and interpretable, as its reasoning is expressed in natural language. And it opens the door to training agents in complex scenarios where multi-modal feedback, including text, visuals, and other sensory inputs, is crucial. Researchers have demonstrated NLRL's potential in games like Maze, Breakthrough, and Tic-Tac-Toe. For example, in maze navigation, an NLRL agent can learn from textual descriptions of the environment and instructions like "reach the goal." In Breakthrough, an NLRL agent can be trained to evaluate board positions and generate insightful annotations, much like a human chess commentator. These early successes show that NLRL can effectively enhance an LLM's reasoning and planning abilities, even with limited training. While promising, NLRL is still in its early stages. Challenges include adapting it to continuous action spaces and high-dimensional states found in robotics. Computational costs associated with using LLMs also need to be addressed. However, the potential of NLRL is immense. Future research aims to establish a more rigorous theoretical foundation for NLRL, explore its integration with other LLM research areas like self-improvement and planning, and expand its applications to broader domains like reasoning tasks and code generation. NLRL offers a compelling vision for the future of AI, where agents can learn and adapt through the very language that shapes our understanding of the world.